docs: fix typos in documentation

Corrected two instances of "developped" to "developed" and fixed a missing character in "our model".

README.md

@@ -100,9 +100,9 @@ The project aims to train sentence embedding models on very large sentence level
 contrastive learning objective. We used the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a 
 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
 
-We developped this model during the 
+We developed this model during the 
 [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
-organized by Hugging Face. We developped this model as part of the project:
+organized by Hugging Face. We developed this model as part of the project:
 [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
 
 ## Intended uses
@@ -126,7 +126,7 @@ We then apply the cross entropy loss by comparing with true pairs.
 
 #### Hyper parameters
 
-We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
+We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
 We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
 a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.